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Record W4388496205 · doi:10.1080/08865655.2023.2276475

Framing Entangled Borders in the Baltic States

2023· article· en· W4388496205 on OpenAlexvenueno aff
Sandra Hagelin

Bibliographic record

VenueJournal of Borderlands Studies · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicCross-Border Cooperation and Integration
Canadian institutionsnot available
Fundersnot available
KeywordsGeopoliticsFraming (construction)SecuritizationPolitical scienceMember statesPolitical economyPublic discourseEuropean unionSociologyDiscourse analysisMedia studiesGender studiesPoliticsLawGeographyInternational tradeEconomics

Abstract

fetched live from OpenAlex

The eastern Baltic borders have become more prominent in public debates following the instrumentalized migration crisis orchestrated by Belarus in 2021. This article explores and assesses the discourses related to borders and border barriers emerging in the Baltic states in reaction to the “crisis”. Using a discourse analysis framework, the work engages with the discursive framing concerned with how to define and consolidate the border and the use of border barriers, as interpreted through media frames. The article situates the framing of borders and border barriers within geopolitical identities and the notion of entangled borders in the EU, referring to the overlapping of nation-state border regimes and the EU external border regime that occurs along the EU’s borderlands. The article finds that media frames relate to three primary discourses: increasing securitization, the Baltic states’ positionality within the EU, and the changing geopolitical space. Discourses on borders and border barriers reveal that different types of borders often entangle in each other, which, through this entanglement, contributes to how borders are constructed, conceived, and expressed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0120.040
Scholarly communication0.0130.010
Open science0.0010.012
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.417
Teacher spread0.373 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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